The exemplary embodiment relates generally to transportation planning and finds particular application in connection with a system and method for reconstructing traveler journeys which take into account inferred user goals to distinguish between multi-segment trips and separate trips.
Fare collection data from automatic ticketing validation (ATV) devices can be used to generate information about a transportation network in which vehicles, such as buses and trams, travel predefined routes at scheduled times. The information may be used to plan new routes or modify existing ones, modify schedules, or adjust pricing to meet various goals, such as improving community satisfaction, reducing costs, reducing traffic congestion, and the like.
In many transportation networks, users pay for trips with smart devices, such as smartcards or smartphone applications. Generally, a radio signal is established between the device and an ATV card reader, e.g., a radiofrequency identification (RFID) tag, to validate the trip. Prior to using the smart device, the user registers with the transportation network, allowing a transportation service provider to associate a charge for the journey with the user's account or to deduct the cost from a stored balance on the smartcard. The ATV device may provide various information for each validation, such as an identifier for the card, the time of the transaction, and the boarding stop or a GPS location, from which the boarding stop can be identified from the predefined route. This information may be collected by the ATV device and downloaded to a central server at the end of each day, or may be transmitted to the central server more frequently, e.g., by using the smartphones of the users as relay devices.
In many instances, users simply exit the vehicle at their chosen stop and no alighting location information is acquired. Additionally, users may make multi-segment trips, in which, in order to reach a destination, the user alights at one stop and boards another vehicle for the next or subsequent segment of the trip.
Systems have been developed to infer the alighting information and identify multi-segment trips using a variety of heuristics, such as:
1. When the smart card is used again within a predefined time period, the cardholder is assumed to be making a multi-segment trip and the alighting stop of the earlier trip is assumed to be the closest on the respective route to the next boarding stop;
2. Users return to their previous trip's destination stop for their next trip.
3. At the end of the day, users return to the first boarding stop of the same or the next day.
4. Alighting stops for travelers without smart cards, such as those using single journey tickets (including multi-trip tickets) follow a similar distribution to those using smart cards.
The next step is to detect the origins and destinations of users. This can be seen as the segmentation of the daily sequence of transport services used by a person into a set of trips motivated by a particular activity. Traditionally, this is performed using a time threshold that is either a maximum duration of a trip or a maximum duration of a transfer. Using this collected information, origin-destination (O-D) matrices can be generated, which, for each pair of stops on a scheduled route or in a transportation network, include a prediction of the number of travelers originating a trip at the first stop and having the second as the destination of their trip. The numbers may be averaged, e.g., over the course of a week, work week, or month.
One problem with this approach is that a multi-segment trip may be inferred when in fact, the user may be making two or more separate trips, i.e., the user has more than one destination. For example, a user may board a first bus at stop A, alight at stop B, board a second bus a few minutes later at stop C, after picking up a cup of coffee, and alight at stop D. The system may infer that the user is making a multi-segment trip, with A as the origin and D (inferred from other information) as the destination, when in fact, the user is making two trips, with destinations B and D, respectively. When a number of such incorrect assumptions is aggregated into an O-D matrix, this may lead to poor transportation planning.